KaBwohe People, what else do u want if its not a #LadiesNight# at Senevin Gardens behind Nuwa Feeds. Kam let's party with man of Rave O'clock mixes @deejmax2003. This Wednesday come have fun n call it a Night to Remember.

Protip: Analyze meetings with Claude w/o having to record or use a 3rd party by turning on closed captioning and pulling the text from the DOM with: document.querySelector('[role="region"][aria-label="Captions"]') Incredible for blunt feedback on how to do better. Prompt like: "file.xml is an XML transcript exported from google meet. Look at my calendar to find the context of the meeting. Analyze .... "

The Times's weekend read: * The Labour leadership contest has already begun. In No 10 Starmer is fighting for his political life, putting huge pressure on teh system to bolster his Premiership in face of existential threat posed by Andy Burnham * Expect a frenzy of activity in coming weeks - the defence investment plan, social media restrictions for under 16s, the Brexit reset. Announcements bogged down in months of bitter internal rows will finally come into public view * All the announcements are coming before or shortly after June 18th, the date of the Makerfield by-election. Starmer is trying to send a message to Labour MPs that he can deliver * He isn't the only one. Burnham has began issuing his own national leadership pledges - starting with a £300million cut in business rates for pubs and small businesses * His press release directly attacked Starmer and Reeves, accusing the government of 'undervaluing' their importance to local communities. This is a *Labour* candidate directly criticising a *Labour* government * The lines between the Labour Party and Burnham's campaign are increasingly blurred. Team Burnham now has a lot of the party machinery on his side - press officers, officials etc - and also has most of the cabinet out knocking doors in Makerfield. Power is already moving * Starmer thinks he can fight Burnham, but his allies are unconvinced. One said that he thinks Burnham has behaved 'appallingly'. 'His view is why should he make it easy for him?' * But there is an acknowledgement that Starmer is on borrowed time. That it is a case of when, not if he goes * There are divides in Team Burnham over when he should amke a move if he wins Makerfield. The 'go-now' camp say he must seize the opportunity or it could slip through his hands, using the momentum of the by-election to act decisively * But others think this could be disastrous - that he needs time to build up a proper plan for government and No 10. That if he doesn't he risks repeating Starmer's mistakes all over again * Then there's what's being billed as the 'battle for the soul of Burnham'. There are those on the left - Louise Haigh, Miatta Fahbulleh and others - who favour a radical break from Starmer. Then there are the centrists - Josh Simons, Jim O'Neill - are are said to be emphasising the importance of fiscal credibility. It is potentially a v unwieldy coalition

‼️How Elite U.S. Journalists Took All-Expenses-Paid Trips to China — and Came Back Changed On July 28, 2026, White House correspondent Natalie Winters dropped a bombshell investigation that ripped the polite mask off a carefully engineered influence operation. A leaked internal document from the **Committee of 100 (C100)** — an organization Xi Jinping himself once praised as a “friendly organization” making “untiring efforts” to advance Chinese influence in the United States — revealed that at least 26 top American journalists, editors, and broadcasters had been flown to China on curated, high-end trips. The goal was not cultural exchange. It was to reshape how they saw Beijing… and how they covered it. ### The Operation C100’s “Leadership Delegation Program” targeted the people who decide what America reads and hears about China. Delegates came from *The New York Times*, *The Washington Post*, *Politico*, *NPR*, *PBS*, *The Atlantic*, *The New Yorker*, *Financial Times*, *TIME*, *USA Today*, and more. These were not backpacker trips. One 2012 delegation alone — Beijing, Shanghai, Hangzhou — cost roughly $60,000. Participants stayed in classic hotels, sampled regional Chinese cuisine, toured the Great Wall and Forbidden City, attended dinners in the Great Hall of the People, and sat down with carefully selected officials, state media executives, business leaders, and academics. C100 tracked its success with clinical precision. It publicly claimed that 70% of American “opinion leaders” who visited China returned with improved views of the country. Internally, the real metric was simpler: Did we change how they think? ### The Smoking-Gun Report The most damning piece is a 2012 after-action report marked **“Internal Use.”** It records what the journalists said after the trip: - Financial Times editor Gary Silverman: “I don’t think that I will ever think or write about China in the future without reflecting on what I learned this week.” - WNYC host Brian Lehrer: His perception of China changed “in 100 ways.” - Foreign Affairs managing editor Jonathan Tepperman returned with a “much deeper, more subtle and more nuanced sense” of China and called it the best press trip he had ever taken. - New York Times deputy business editor Winnie O’Kelley came back with “several story ideas” and plans to “shape my staff across Asia in some different ways.” She even hoped to add at least one business reporter in China. A Chinese academic congratulated the organizers: “It is hard to speak on China without being to the country. You made the change!” C100’s own verdict was blunt: “The C-100 Leadership Delegation Program has had a visible impact on their understanding and perceptions of China.” They hoped the delegates would share those new perceptions with colleagues and reshape coverage back home. ### The Names Here are some of the participants (titles at the time of their trips): - Jill Abramson — Managing Editor, *The New York Times* - David Brooks — Columnist, *The New York Times* - David Ignatius — Associate Editor & columnist, *Washington Post* - Eugene Robinson — Columnist & Associate Editor, *Washington Post* - Ruth Marcus — Columnist, *Washington Post* - Fred Hiatt — Editorial Page Editor, *Washington Post* - John Harris — Editor-in-Chief, *Politico* - Juan Williams — Senior Political Analyst, *NPR* - Brian Lehrer — Host, WNYC - Clive Crook — Senior Editor, *The Atlantic* - Jonathan Tepperman — Managing Editor, *Foreign Affairs* - Gary Silverman — U.S. News Editor, *Financial Times* - And many more from *Newsweek*, *TIME*, *Los Angeles Times*, *USA Today*, *HuffPost*, and *The New Yorker*. ### What Happened After They Came Home Winters tracked the subsequent work of several delegates. Some of the same voices later became prominent critics of tougher policies toward Beijing: - David Brooks called Trump’s proposed tariffs “the single worst policy idea on the table.” - Eugene Robinson described the trade war as an “ill-advised gambit” and suggested Xi Jinping could emerge as the “reasonable adult.” - David Ignatius warned America was “dramatically overestimating China’s capabilities” and called growing alarm “scare talk.” One Financial Times columnist later defended allowing the Chinese-founded fast-fashion giant Shein to list in London despite serious supply-chain and labor concerns. ### The Bigger Picture This was not random tourism. C100 has repeatedly been linked to figures and institutions inside the Chinese Communist Party’s United Front system — the same influence network U.S. officials describe as designed to “co-opt and neutralize sources of opposition.” The organization selected journalists based on their ability to deliver favorable coverage and then measured success by whether those journalists’ thinking actually shifted. The documents, itineraries, and internal assessments were scattered across archives for years. Natalie Winters pulled them together, named the names, and published the evidence. Full investigation and original documents:

Everyone talks about AI enabling the "one-person Hollywood studio." I spent the last two months putting that to the test. The result? A full, 2+ hour feature film. Introducing "The Four Classics." Using @suno, @soraofficialapp , @grok Imagine 1.0, and Genie 3 by @GoogleLabs , I adapted China’s greatest literary epics into a sprawling, cyberpunk hip-hop opera. Grab some popcorn. Welcome to the future of AI film production. 🍿🎥 Timestamps: Water Margin 0:00:09 “The Tiger (十八婉)”- Wu Song rejects the law and engages in a drunken battle defeating the Tiger 0:03:20 “Forced to the Mountain (逼上梁山)”- Lin Chong seeks revenge on Gao Qiu for destroying his life and familial honor 0:09:24 “Black Whirlwind (黑俊峰)”- Introduction of the brute Li Kui and his love for violence 0:11:42 “The Gathering / 108 (大聚义)”- The Liangshan heroes formally assemble to announce their reign at the Hall of Loyalty and Righteousness 0:14:08 “The Amnesty (招安)”- Song Jiang, leader of the heroes, debates the pros of accepting an offer of Amnesty from the Emperor 0:18:05 “The Poison Wine (神聚蓼儿洼)”- Song Jiang encourages Li Kui to join him in drinking Poisoned Wine gifted by the Emperor Romance of the Three Kingdoms 0:22:20 “The Oath (桃园)”- Liu Bei is a small time hustler with ambition to take over the nation. He makes a blood oath with Guan Yu and Zhang Fei to do so. 0:25:57 “The Mastermind (卧龙)”- Liu Bei recruits the strategist Zhuge Liang to join his cause and take over the kingdom 0:30:49 “The Burden (​仁义)”- Caocao’s troops attack causing Liu Bei and Shu to flee south. Liu Bei burns the city of Xinye and uses common people as human shields to make his escape. 0:33:44 “The License (挟天子)”- Caocao plans his move to crush the growing nation of Shu from his penthouse 0:36:45 “No Regrets (​宁负)”- Flashback to Caocao’s betrayal of his ally Old Lu. He chooses to massacre a banquet after hearing the sound of knives sharpening. An examination of ruthless ambition. 0:39:59 “The Flex (​八十万)”- Caocao’s army of 800k soldiers races south to destroy Liu Bei’s Shu. 0:43:26 “The Estate (​江东)”- Sun Quan, leader of Wu, forms an unlikely alliance with Liu Bei to quell the approach of Caocao’s army 0:47:27 “The Shift (东风)”- Zhuge Liang summons the East Wind to change the fate of Caocao’s army at the Red Cliffs 0:51:15 “The Chains (​连环)”- Pang Tong of Shu deceives Caocao into chaining his fleet of ships together setting the perfect trap 0:54:37 “Symphony No.7 (​​第七交响曲)”- General Zhou Yu of Wu sparks the trap set by Shu strategists lighting Caocao’s entire fleet ablaze. The Epic Battle of the Red Cliffs concludes Caocao’s push south. 0:59:25 “The Split (​​三分)”- Opening Credits. Sima Yi reviews the status of the Three Kingdoms after The Red Cliffs result in an uneasy truce. 1:02:23 “Still G.U.A.N.”- Following the war, Guan Yu runs operations in Jingzhou. Sun Quan sends a formal message requesting Guan Yu’s daughter marry his son strengthening the alliance. Guan Yu promptly refuses insulting the leader of Wu. 1:05:58 “The Statue (刮骨)”- After an assassination attempt, Guan Yu is struck with poison that makes its way into his bone. He requires immediate, invasive surgery. He survives through his grit, maintaining his arrogance, but weakened from the procedure. 1:09:42 “The Funeral (白衣)”- Guan Yu is ambushed by Wu’s forces after retreating to the mountains of Maicheng. The Wu assassins approach as merchants, disguised in white clothing, catching Guan Yu by surprise. 1:13:19 “The Lie (桃园)”- Liu Bei receives news of Guan Yu’s death and sets out on a war path against Wu. He loses his composure leaning into pills to cope with the loss of his blood brothers. 1:17:08 “The O.D. (七百里)”- Liu Bei’s madness leads him to make a fatal strategic error. He stretches his army thin in a 700 mile long “snake” formation. 1:20:32 “The Boomarang (​回旋镖)”- Liu Bei’s army burns to ashes in a brutal defeat reminiscent of his triumph at the Red Cliffs. 1:23:46 “The Flop (​扶不起)”- Zhuge Liang tries to pick up the ashes of Shu nation by educating Liu Bei’s son Adou. Adou is too busy enjoying his life and inherited wealth to bother with the burden of leadership. 1:27:48 “The Frequency (​空城)”- Cornered by the army of Wei led by Sima Yi, Zhugeliang employes the empty city strategy to outsmart his rival. 1:31:12 “The Flatline (​五丈原)”- Zhuge Liang tries to outlive his corporeal form to ensure the longevity of the nation of Shu and prevail in the war. 1:34:33 “The Liquidation (​归晋)”- Sima Yi consolidates control of the Three Kingdoms and unifies China under Wei. Journey to the West 1:38:20 “Eight Trigrams Furnace (炼丹炉)”- A family flees the grind of life in China in search of “Freedom” offered by the American Dream 1:41:36 “White Bone Spirit (白骨精)”- The family experiences the demons and tribulations of modern American life in their struggle to assimilate 1:45:33 “Thunderclap Temple (雷音寺)”- The family realizes the truth of the Blank Scriptures. Dream of the Red Chamber 1:49:39 “顽石GENESIS BLOCK (Final Cut)” - Rejecting its status as a useless system glitch, the sentient Stone signs a smart contract to download its consciousness into the mortal avatar of Jia Baoyu. Trading the safety of eternal numbness for the sensory overload of a physical body, Baoyu logs into the simulation specifically to chase the lust, trauma, and "pain" of human existence. 1:54:17 “大观园THE GARDEN (HYPE HOUSE)” - Inside the simulation, Baoyu lives as the pampered admin of the Grand View Garden, a walled-off, hyper-capitalist echo chamber where the elite ignore the rotting code of the outside world. Surrounded by the Twelve Beauties, he gorges on digital excess and mindless consumption—literally "eating the rouge"—pushing the server to the absolute brink of failure. 1:57:45 “葬花DELETING ASSETS (THE CRASH)”- The manic party violently ends with a massive system crash, forcing Baoyu to watch the algorithmic destruction of his decadent world. Amidst the digital decay, his tragic soulmate Lin Daiyu desperately tries to save the pure, beautiful files of their love, burying her fragile memories to protect them from the corrupted stream before she is permanently deleted. 2:01:09 “白茫茫WHITE NOISE (LOGOUT)”- The corrupted simulation undergoes a complete format, wiping out the Jia family's vast digital empire and leaving nothing behind but a stark, quiet white wasteland.

$AMD| The FOMO to buy @AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: @WSJ yesterday came out with an article that @OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from @AnthropicAI has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited @AnthropicAI is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why @OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

One of the best performing stocks of the last 30 years sells car parts O'Reilly Auto Parts $ORLY went public in 1993 A $10,000 investment at IPO is worth over $4,900,000 today The best trades are never where everyone is looking